LAION · Complete annotated recording · CC BY 4.0

One performance.
Every annotation, explained.

German-English Voice Acting Challenges · 50-audio comparison · Exact grammar

The Last Sunset on Our Beach

de-00001 · DE · generation voice Charon · 33.74 seconds

Original media bytes are retained. This self-contained page also works offline.

MOSS whole-audio description

A single young-adult to mature-adult masculine speaker delivers an intimate, deeply contemplative monologue in Standard German. The vocal production is exceptionally quiet, soft-spoken, and breathy, hovering between a close-mic whisper and a gentle half-voice. The tempo is slow and measured, broken into short, reflective phrases separated by prominent, audible inhalations and weary sighs. The timbre is warm and rounded with low-mid chest resonance, low tension, and minimal metallic edge, creating an ASMR-adjacent, confessional atmosphere. The emotional tone is dominated by wistful longing, gentle sadness, and quiet vulnerability, with a subtle undercurrent of helplessness as the speaker reflects on the passing of time. The recording is pristine, dry, and close, with virtually no background noise, allowing the delicate breath dynamics and subtle vocal fry to remain fully audible and organically integrated.

Humaneness Ears MediumEncoder-only acoustic scores, embeddings and proposed intervals.
MOSS voice adapterOne complete-audio voice description.
MOSS burst adapterComplete audio plus an event's start and end.
Parakeet + rulesASR words, measured durations and procedural wording.

Predicted vocal events

Threshold 0.6, 0.08-s minimum duration, overlap suppression at half the shorter interval, no event-count cap. Type probabilities are conditional class softmax, not calibrated detection confidence. Click an interval to seek.

3.72–4.86 s6.30–7.04 s6.86–7.82 s12.84–13.90 s13.56–14.66 s26.22–27.32 s

Duration 1.14 s · onset probability 0.753
Breath: 60.0% · Exasperated Sigh: 9.3% · Sharp Inhale: 7.7%

A slow, audible, breathy inhalation and soft exhalation occurs in the pause between 'glänzt' and 'Fast genau wie damals', conveying quiet contemplation and emotional weight.

Duration 0.74 s · onset probability 0.748
Breath: 28.8% · Exasperated Sigh: 20.9% · Sharp Inhale: 9.9%

A slow, audible inhalation and soft exhalation occurs in the pause after 'Fast genau wie damals', reflecting quiet contemplation and emotional grounding.

Duration 0.96 s · onset probability 0.775
Breath: 50.2% · Exasperated Sigh: 19.0% · Sharp Inhale: 4.8%

A slow, audible breath intake and release during a pause, reflecting quiet contemplation and emotional weight.

Duration 1.06 s · onset probability 0.828
Breath: 32.0% · Exasperated Sigh: 23.4% · Exhausted Groan: 7.4%

A slow, audible breath intake and release between 'deine Hand' and 'sie fühlt so kühl', emphasizing tactile intimacy and emotional vulnerability.

Duration 1.10 s · onset probability 0.702
Breath: 38.6% · Exasperated Sigh: 19.8% · Wistful Sigh: 12.3%

A slow, audible breath intake and release occurs in the pause before 'Sie fühlt so kühl', conveying physical closeness and emotional vulnerability.

Duration 1.10 s · onset probability 0.882
Exasperated Sigh: 57.7% · Exhausted Groan: 7.3% · Breath: 5.0%

A soft, tremulous exhalation and quiet sob-like release following 'untergeht', conveying deep emotional helplessness and sorrow.

Procedural GENERAL and SCRIPT

Threshold 0.7; two distinctive voice properties, up to one emotion, compact tags. This independently selected event list can differ from MOSS. Content Enjoyment supplies pleasantness wording.

GENERAL: very whispered, very noisy, very slow, very burnout; vocal-burst candidates are marked in the timed event list
SCRIPT:
(very denasal, very full, awestruck, enjoyable) [0.47 seconds duration] Schau
[0.25 seconds pause]
(very denasal, very full, awestruck, enjoyable) [0.06 seconds duration] mal,
[0.50 seconds pause]
(very denasal, very full, awestruck, enjoyable) [1.60 seconds duration] wie golden das Wasser glänzt.
[0.84 seconds pause]
(Breath, 1.14 seconds)
(very naturalistic, very denasal, wistfulness) [2.96 seconds duration] Fast genau wie damals. (Vocal Burst, 0.74 seconds) (Breath, 0.96 seconds)
[0.50 seconds pause]
(very naturalistic, very organic, slightly contemplation) [1.83 seconds duration] Der Sand unter uns ist noch ganz warm.
[1.61 seconds pause]
(very whispered, very organic, very lethargy) [1.06 seconds duration] Aber deine Hand,
(very whispered, very organic, very lethargy) [2.83 seconds duration] (Vocal Burst, 1.06 seconds) (Vocal Burst, 1.10 seconds) sie wird so kühl.
[2.03 seconds pause]
(very denasal, very naturalistic, very despair, enjoyable) [3.32 seconds duration] Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht.
[0.92 seconds pause]
(very steady, very full, slightly nostalgia) [1.74 seconds duration] Wie Zeit, die uns wegrutscht.
[0.74 seconds pause]
(very fading, very naturalistic, very apathy) [2.92 seconds duration] Ich will nicht, dass die Sonne untergeht. (Exasperated Sigh, 1.10 seconds)
[1.48 seconds pause]
(very fading, very flat, very detachment, less-enjoyable) [0.38 seconds duration] Bitte.
[2.10 seconds pause]
(very denasal, hi-fi, slightly zeal) [1.71 seconds duration] Ich bin doch überhaupt nicht bereit dafür.
[0.75 seconds pause]

All ASR words are retained exactly once. Every selected burst and speech segment has a numeric duration. Inline overlapping events are included in the enclosing segment's time budget. Verification checks serialization; it does not establish acoustic accuracy.

Choose a Humaneness Voice training representation

Two caption choices × three transcript choices. The no-appended-event caption retains the original whole-audio description, which can itself mention vocal events. Plain transcript is verbatim ASR; timed variants use the MOSS intervals. Original intended generation text is not substituted for ASR.

All 192 regression targets, plus CPS and count

Raw values remain on the original target scales. Bars and ranking use reference-relative z-scores from the preserved 118,692-clip mixed reference; the procedural writer uses a separate 80,265-clip speech reference. z is (prediction − mean) / population standard deviation, not a probability. Bars are clipped at ±3 only for display.

40 emotion predictions
TargetRawz−3 · 0 · +3
Fatigue Exhaustion
emo_Fatigue_Exhaustion
2.615+1.56
Sadness
emo_Sadness
2.047+1.48
Contemplation
emo_Contemplation
2.797+1.46
Helplessness
emo_Helplessness
2.328+1.24
Disappointment
emo_Disappointment
1.725+1.18
Doubt
emo_Doubt
1.583+1.16
Contentment
emo_Contentment
0.165-1.14
Distress
emo_Distress
2.215+1.01
Longing
emo_Longing
1.005+1.00
Hope Enthusiasm Optimism
emo_Hope_Enthusiasm_Optimism
0.178-0.94
Pride
emo_Pride
0.012-0.89
Interest
emo_Interest
1.128-0.83
Pain
emo_Pain
0.969+0.79
Emotional Numbness
emo_Emotional_Numbness
0.492+0.75
Amusement
emo_Amusement
0.041-0.75
Elation
emo_Elation
-0.084-0.63
Teasing
emo_Teasing
-0.024-0.63
Triumph
emo_Triumph
-0.055-0.57
Astonishment Surprise
emo_Astonishment_Surprise
0.049-0.56
Embarrassment
emo_Embarrassment
0.013-0.45
Relief
emo_Relief
0.097-0.42
Confusion
emo_Confusion
0.645+0.40
Pleasure Ecstasy
emo_Pleasure_Ecstasy
0.004-0.38
Infatuation
emo_Infatuation
-0.030-0.38
Fear
emo_Fear
0.673+0.38
Shame
emo_Shame
0.019-0.31
Sexual Lust
emo_Sexual_Lust
-0.014-0.28
Thankfulness Gratitude
emo_Thankfulness_Gratitude
0.056-0.26
Anger
emo_Anger
0.166-0.21
Affection
emo_Affection
0.317-0.20
Disgust
emo_Disgust
0.056-0.19
Contempt
emo_Contempt
0.067-0.18
Impatience and Irritability
emo_Impatience_and_Irritability
0.381-0.17
Bitterness
emo_Bitterness
0.396+0.14
Malevolence Malice
emo_Malevolence_Malice
0.007-0.14
Intoxication Altered States of Consciousness
emo_Intoxication_Altered_States_of_Consciousness
0.020-0.11
Sourness
emo_Sourness
0.236-0.11
Awe
emo_Awe
0.236+0.10
Jealousy and Envy
emo_Jealousy_and_Envy
0.014+0.07
Concentration
emo_Concentration
1.647-0.03
57 VoiceNet dimensions
TargetRawz−3 · 0 · +3
VoiceNet R_NASL
vn_R_NASL_reg
0.771-2.47
VoiceNet DARC
vn_DARC_reg
2.055-2.03
VoiceNet VFLX
vn_VFLX_reg
2.317-2.03
VoiceNet R_ORAL
vn_R_ORAL_reg
2.564-1.98
VoiceNet R_MASK
vn_R_MASK_reg
1.558-1.94
VoiceNet RANG
vn_RANG_reg
1.624-1.87
VoiceNet HARM
vn_HARM_reg
1.783-1.83
VoiceNet S_WHIS
vn_S_WHIS_reg
2.854+1.76
VoiceNet METL
vn_METL_reg
0.779-1.67
VoiceNet S_ASMR
vn_S_ASMR_reg
2.583+1.66
VoiceNet R_HEAD
vn_R_HEAD_reg
0.943-1.54
VoiceNet ATCK
vn_ATCK_reg
1.835-1.52
VoiceNet S_PLAY
vn_S_PLAY_reg
-0.189-1.50
VoiceNet BRGT
vn_BRGT_reg
2.459-1.47
VoiceNet TEMP
vn_TEMP_reg
1.808-1.43
VoiceNet STNC
vn_STNC_reg
1.262-1.43
VoiceNet AROU
vn_AROU_reg
1.634-1.42
VoiceNet R_MIXD
vn_R_MIXD_reg
2.599-1.38
VoiceNet S_MONO
vn_S_MONO_reg
3.904+1.29
VoiceNet ARSH
vn_ARSH_reg
2.522-1.18
VoiceNet RESP
vn_RESP_reg
3.856+1.13
VoiceNet VALN
vn_VALN_reg
1.579-1.09
VoiceNet ROUG
vn_ROUG_reg
2.848+1.09
VoiceNet VULN
vn_VULN_reg
4.183+0.98
VoiceNet EMPH
vn_EMPH_reg
2.590-0.97
VoiceNet SMTH
vn_SMTH_reg
2.369-0.96
VoiceNet S_AUTH
vn_S_AUTH_reg
0.833-0.96
VoiceNet CHNK
vn_CHNK_reg
1.804-0.96
VoiceNet CLRT
vn_CLRT_reg
3.062-0.94
VoiceNet S_CART
vn_S_CART_reg
-0.011-0.93
VoiceNet REGS
vn_REGS_reg
1.159-0.90
VoiceNet FOCS
vn_FOCS_reg
3.394-0.89
VoiceNet S_RANT
vn_S_RANT_reg
0.158-0.78
VoiceNet S_TECH
vn_S_TECH_reg
0.654-0.77
VoiceNet R_THRT
vn_R_THRT_reg
3.097+0.76
VoiceNet S_CONV
vn_S_CONV_reg
2.604-0.76
VoiceNet GEND
vn_GEND_reg
4.628+0.73
VoiceNet ESTH
vn_ESTH_reg
3.198-0.70
VoiceNet TENS
vn_TENS_reg
2.989+0.61
VoiceNet R_CHST
vn_R_CHST_reg
3.321+0.53
VoiceNet S_NEWS
vn_S_NEWS_reg
0.245-0.52
VoiceNet VALS
vn_VALS_reg
2.524-0.50
VoiceNet STRU
vn_STRU_reg
3.104-0.49
VoiceNet S_DRAM
vn_S_DRAM_reg
3.022+0.45
VoiceNet COGL
vn_COGL_reg
2.290+0.42
VoiceNet BKGN
vn_BKGN_reg
3.565-0.36
VoiceNet S_FORM
vn_S_FORM_reg
1.344-0.29
VoiceNet S_CASU
vn_S_CASU_reg
2.975-0.28
VoiceNet WARM
vn_WARM_reg
3.300+0.25
VoiceNet DFLU
vn_DFLU_reg
1.747+0.25
VoiceNet AGEV
vn_AGEV_reg
3.191+0.19
VoiceNet EXPL
vn_EXPL_reg
0.058+0.18
VoiceNet RCQL
vn_RCQL_reg
3.817+0.18
VoiceNet S_NARR
vn_S_NARR_reg
1.949-0.17
VoiceNet VOLT
vn_VOLT_reg
1.970-0.10
VoiceNet FULL
vn_FULL_reg
3.207+0.08
VoiceNet S_STRY
vn_S_STRY_reg
2.122-0.03
Quality, enjoyment, genuineness, blend, CPS and remaining targets
TargetRawz−3 · 0 · +3
estimated burst count
estimated_burst_count
32.000+2.19
voiceclap attribute:Fatigue Exhaustion
voiceclap_attribute:Fatigue_Exhaustion
2.184+1.75
burst count log1p
burst_count_log1p
1.902+1.42
voiceclap attribute:Helplessness
voiceclap_attribute:Helplessness
2.099+1.41
voiceclap attribute:Pain
voiceclap_attribute:Pain
1.099+1.41
voiceclap attribute:Sadness
voiceclap_attribute:Sadness
1.550+1.40
voiceclap attribute:duration
voiceclap_attribute:duration
24.253+1.36
voiceclap attribute:Interest
voiceclap_attribute:Interest
0.638-1.35
voiceclap attribute:Intoxication Altered States of Consciousness
voiceclap_attribute:Intoxication_Altered_States_of_Consciousness
0.203+1.32
voiceclap attribute:Longing
voiceclap_attribute:Longing
0.645+1.29
voiceclap attribute:Distress
voiceclap_attribute:Distress
2.408+1.26
voiceclap attribute:Disappointment
voiceclap_attribute:Disappointment
1.553+1.22
voiceclap attribute:Fear
voiceclap_attribute:Fear
0.689+1.15
voiceclap attribute:Hope Enthusiasm Optimism
voiceclap_attribute:Hope_Enthusiasm_Optimism
0.083-1.10
voiceclap attribute:Contentment
voiceclap_attribute:Contentment
0.304-1.06
voiceclap attribute:Shame
voiceclap_attribute:Shame
0.237+1.03
voiceclap attribute:Teasing
voiceclap_attribute:Teasing
-0.139-1.03
voiceclap attribute:Amusement
voiceclap_attribute:Amusement
-0.130-1.03
voiceclap attribute:Confident vs. Hesitant
voiceclap_attribute:Confident_vs._Hesitant
0.992-0.96
voiceclap attribute:Emotional Numbness
voiceclap_attribute:Emotional_Numbness
0.272+0.96
voiceclap attribute:score speech quality
voiceclap_attribute:score_speech_quality
1.872-0.95
eiv extra:score speech quality
eiv_extra:score_speech_quality
1.778-0.93
voiceclap attribute:Background Noise
voiceclap_attribute:Background_Noise
0.664+0.91
voiceclap attribute:Bitterness
voiceclap_attribute:Bitterness
0.666+0.90
voiceclap attribute:Contempt
voiceclap_attribute:Contempt
-0.156-0.88
voiceclap attribute:Vulnerable vs. Emotionally Detached
voiceclap_attribute:Vulnerable_vs._Emotionally_Detached
0.901-0.81
voiceclap attribute:talking speed
voiceclap_attribute:talking_speed
8.696-0.78
voiceclap attribute:Elation
voiceclap_attribute:Elation
-0.131-0.77
voiceclap attribute:Astonishment Surprise
voiceclap_attribute:Astonishment_Surprise
0.045-0.77
voiceclap attribute:Doubt
voiceclap_attribute:Doubt
0.928+0.76
voiceclap attribute:score background quality
voiceclap_attribute:score_background_quality
3.661+0.73
voiceclap attribute:Pride
voiceclap_attribute:Pride
0.068-0.72
voiceclap attribute:Triumph
voiceclap_attribute:Triumph
-0.068-0.67
voiceclap attribute:Concentration
voiceclap_attribute:Concentration
1.166-0.67
eiv extra:Confident vs. Hesitant
eiv_extra:Confident_vs._Hesitant
0.953-0.66
voiceclap attribute:Warm vs. Cold
voiceclap_attribute:Warm_vs._Cold
-0.135-0.65
eiv extra:Monotone vs. Expressive
eiv_extra:Monotone_vs._Expressive
1.512-0.65
voiceclap attribute:Pleasure Ecstasy
voiceclap_attribute:Pleasure_Ecstasy
-0.077-0.63
voiceclap attribute:Valence
voiceclap_attribute:Valence
-0.571-0.62
voiceclap attribute:Serious vs. Humorous
voiceclap_attribute:Serious_vs._Humorous
0.686-0.61
audiobox:PQ
audiobox:PQ
7.504+0.58
voiceclap attribute:Sexual Lust
voiceclap_attribute:Sexual_Lust
0.072+0.57
voiceclap attribute:Jealousy & Envy
voiceclap_attribute:Jealousy_&_Envy
0.069+0.57
voiceclap attribute:Arousal
voiceclap_attribute:Arousal
1.307-0.55
characters per second
characters_per_second
9.891-0.55
genuineness 0 6
genuineness_0_6
4.093-0.54
eiv extra:Gender
eiv_extra:Gender
-0.354-0.53
eiv extra:score content enjoyment
eiv_extra:score_content_enjoyment
4.826+0.52
voiceclap attribute:score content enjoyment
voiceclap_attribute:score_content_enjoyment
4.865+0.51
eiv extra:Submissive vs. Dominant
eiv_extra:Submissive_vs._Dominant
0.562+0.50
eiv extra:score background quality
eiv_extra:score_background_quality
3.662+0.50
voiceclap attribute:Relief
voiceclap_attribute:Relief
0.428+0.50
voiceclap attribute:score overall quality
voiceclap_attribute:score_overall_quality
2.906+0.48
voiceclap attribute:Malevolence Malice
voiceclap_attribute:Malevolence_Malice
-0.043-0.48
eiv extra:High-Pitched vs. Low-Pitched
eiv_extra:High-Pitched_vs._Low-Pitched
1.816+0.47
dnsmos:BAK
dnsmos:BAK
3.970+0.47
eiv extra:score overall quality
eiv_extra:score_overall_quality
2.958+0.47
eiv extra:Valence
eiv_extra:Valence
0.015-0.46
dnsmos:BAK raw
dnsmos:BAK_raw
4.094+0.45
voiceclap attribute:Monotone vs. Expressive
voiceclap_attribute:Monotone_vs._Expressive
1.656-0.44
voiceclap attribute:Contemplation
voiceclap_attribute:Contemplation
1.791+0.41
voiceclap attribute:Sourness
voiceclap_attribute:Sourness
0.409+0.39
voiceclap attribute:Disgust
voiceclap_attribute:Disgust
0.010-0.36
voiceclap attribute:Recording Quality
voiceclap_attribute:Recording_Quality
2.325-0.32
voiceclap attribute:Infatuation
voiceclap_attribute:Infatuation
0.070+0.31
eiv extra:Authenticity
eiv_extra:Authenticity
3.029+0.31
eiv extra:Recording Quality
eiv_extra:Recording_Quality
2.362-0.30
audiobox:PC
audiobox:PC
1.751+0.29
voiceclap attribute:Age
voiceclap_attribute:Age
3.160+0.29
voiceclap attribute:Awe
voiceclap_attribute:Awe
0.091-0.28
eiv extra:Background Noise
eiv_extra:Background_Noise
0.492+0.26
eiv extra:Vulnerable vs. Emotionally Detached
eiv_extra:Vulnerable_vs._Emotionally_Detached
1.177-0.24
eiv extra:Arousal
eiv_extra:Arousal
1.543-0.22
voiceclap attribute:Submissive vs. Dominant
voiceclap_attribute:Submissive_vs._Dominant
0.509+0.22
voiceclap attribute:High-Pitched vs. Low-Pitched
voiceclap_attribute:High-Pitched_vs._Low-Pitched
1.708+0.21
voiceclap attribute:Thankfulness Gratitude
voiceclap_attribute:Thankfulness_Gratitude
0.234+0.18
dnsmos:OVRL raw
dnsmos:OVRL_raw
3.468+0.18
eiv extra:Serious vs. Humorous
eiv_extra:Serious_vs._Humorous
0.923-0.17
dnsmos:OVRL
dnsmos:OVRL
3.071+0.17
voiceclap attribute:Impatience and Irritability
voiceclap_attribute:Impatience_and_Irritability
0.532+0.17
voiceclap attribute:Gender
voiceclap_attribute:Gender
0.128-0.14
voiceclap attribute:Anger
voiceclap_attribute:Anger
0.200-0.13
audiobox:CE
audiobox:CE
5.318-0.13
eiv extra:Warm vs. Cold
eiv_extra:Warm_vs._Cold
0.194-0.12
voiceclap attribute:Confusion
voiceclap_attribute:Confusion
0.357+0.08
voiceclap attribute:Affection
voiceclap_attribute:Affection
0.411+0.08
voiceclap attribute:Soft vs. Harsh
voiceclap_attribute:Soft_vs._Harsh
0.396+0.07
dnsmos:P808 MOS
dnsmos:P808_MOS
3.590-0.07
dnsmos:SIG raw
dnsmos:SIG_raw
3.697-0.06
dnsmos:SIG
dnsmos:SIG
3.338-0.06
eiv extra:Soft vs. Harsh
eiv_extra:Soft_vs._Harsh
0.377-0.06
eiv extra:Age
eiv_extra:Age
3.136+0.05
voiceclap attribute:Authenticity
voiceclap_attribute:Authenticity
2.979+0.05
voiceclap attribute:Embarrassment
voiceclap_attribute:Embarrassment
0.082-0.04
audiobox:CU
audiobox:CU
6.175+0.04
blend 0 10
blend_0_10
7.740+0.03
R quality
R_quality
0.832+0.01
Predicted Orange embeddings: 128D timbre and 250D identity

These student predictions approximate the original embedding teachers; they do not establish a person's actual identity.

{
  "orange_timbre_128d": [
    -0.04983147606253624,
    0.023882592096924782,
    -0.10610577464103699,
    -0.0997358113527298,
    0.008297921158373356,
    -0.09964712709188461,
    -0.03593664616346359,
    0.023651329800486565,
    0.06697720289230347,
    -0.0924522876739502,
    0.20571409165859222,
    -0.011516612954437733,
    0.05164948105812073,
    -0.08872520178556442,
    -0.07791733741760254,
    -0.06398153305053711,
    -0.1117740347981453,
    -0.07198496907949448,
    0.06820324063301086,
    -0.08438748866319656,
    0.16487917304039001,
    0.05484118312597275,
    -0.0926118865609169,
    -0.04861260578036308,
    -0.09429089725017548,
    -0.0910421684384346,
    -0.029078027233481407,
    -0.05176115781068802,
    -0.06215202808380127,
    0.12419476360082626,
    0.1089307963848114,
    0.0925208181142807,
    -0.0588122233748436,
    -0.04414619132876396,
    -0.10966892540454865,
    0.24869053065776825,
    -0.10426044464111328,
    0.01955111138522625,
    -0.08710084110498428,
    -0.10220997780561447,
    -0.09901589900255203,
    -0.11599769443273544,
    -0.06689763069152832,
    -0.10077015310525894,
    0.24835310876369476,
    -0.08648348599672318,
    -0.019590899348258972,
    -0.12266386300325394,
    -0.11799351125955582,
    0.0491182766854763,
    0.060477349907159805,
    -0.02687610127031803,
    0.10162234306335449,
    0.06204751878976822,
    -0.0650954470038414,
    0.14747367799282074,
    0.09298334270715714,
    -0.10431508719921112,
    -0.08620548993349075,
    0.028218740597367287,
    -0.07901781797409058,
    0.1483258754014969,
    -0.07442032545804977,
    0.01107813697308302,
    0.048053257167339325,
    -0.094272680580616,
    0.09073445945978165,
    -0.0988701805472374,
    0.10603292286396027,
    -0.09497438371181488,
    0.0841708555817604,
    0.039952028542757034,
    -0.09752141684293747,
    0.0005181399756111205,
    -0.021209031343460083,
    -0.019901476800441742,
    0.01118406467139721,
    0.08703230321407318,
    0.0013645407743752003,
    -0.0694628655910492,
    0.03915470093488693,
    0.08340346068143845,
    0.02051367424428463,
    -0.017722204327583313,
    0.05281371250748634,
    0.08200916647911072,
    0.14894993603229523,
    -0.009487091563642025,
    0.06415023654699326,
    0.016483550891280174,
    -0.11295408755540848,
    0.20560479164123535,
    -0.10499858111143112,
    -0.004711559042334557,
    -0.010504337027668953,
    0.0008507116581313312,
    -0.09244557470083237,
    -0.06340733170509338,
    0.03034818544983864,
    -0.02579406090080738,
    0.09905904531478882,
    0.07521548867225647,
    0.08731508255004883,
    -0.11588840186595917,
    -0.051474057137966156,
    -0.07182537019252777,
    -0.042012572288513184,
    0.03049173392355442,
    0.16161222755908966,
    -0.09537988156080246,
    0.026091240346431732,
    -0.10134435445070267,
    -0.1000320166349411,
    -0.012575422413647175,
    -0.11623924970626831,
    0.09887256473302841,
    -0.08654724061489105,
    0.03970590978860855,
    -0.03912954404950142,
    0.16081945598125458,
    -0.02512662298977375,
    -0.012262250296771526,
    -0.07197108119726181,
    -0.04720009118318558,
    0.03151121735572815,
    -0.021381694823503494,
    -0.08782985806465149,
    0.1260218769311905
  ],
  "orange_identity_250d": [
    -0.08017516136169434,
    -0.04697108268737793,
    -0.023682555183768272,
    -0.04240821674466133,
    0.02732660062611103,
    -0.05781247466802597,
    0.051901888102293015,
    0.08717372268438339,
    0.00547076016664505,
    0.05490074306726456,
    -0.03769021853804588,
    0.01832989789545536,
    -0.021137867122888565,
    -0.07172944396734238,
    -0.09888685494661331,
    0.10896465182304382,
    -0.060961294919252396,
    0.08402490615844727,
    -0.01852688379585743,
    -0.04651955887675285,
    0.03301751986145973,
    -0.05272827297449112,
    0.019229575991630554,
    0.030205167829990387,
    0.07397162169218063,
    -0.02379758469760418,
    -0.05233076214790344,
    0.06562010943889618,
    0.12665806710720062,
    -0.020517179742455482,
    0.04940852150321007,
    -0.03464426100254059,
    -0.03757864236831665,
    0.021381082013249397,
    0.03180048614740372,
    0.032644350081682205,
    0.14495831727981567,
    -0.024882107973098755,
    -0.0975164845585823,
    -0.03537474945187569,
    -0.033714957535266876,
    -0.032381124794483185,
    0.050255872309207916,
    -0.014267432503402233,
    0.030649779364466667,
    0.010242458432912827,
    -0.006765435449779034,
    -0.02773023582994938,
    0.032653119415044785,
    0.012189097702503204,
    -0.047851596027612686,
    -0.0012340941466391087,
    0.006127479951828718,
    -0.009498809464275837,
    0.03165580704808235,
    0.01514094602316618,
    -0.026761701330542564,
    -0.017197459936141968,
    -0.05620838701725006,
    0.08087608218193054,
    -0.05105797201395035,
    -0.04922724887728691,
    -0.06058121100068092,
    -0.04398611560463905,
    0.03971969336271286,
    0.05562954768538475,
    -0.017495619133114815,
    0.10652361065149307,
    -0.05982103943824768,
    0.02571728639304638,
    -0.07251051813364029,
    0.06497842818498611,
    -0.03167323023080826,
    0.05153911933302879,
    -0.056190963834524155,
    0.10473819822072983,
    0.08438756316900253,
    0.007102253381162882,
    -0.015668829903006554,
    0.09301820397377014,
    -0.05515177547931671,
    -0.03183533623814583,
    -0.027595054358243942,
    -0.06738798320293427,
    0.02115180715918541,
    -0.040852975100278854,
    -0.004098452627658844,
    0.07027184218168259,
    -0.017852095887064934,
    -0.005180888809263706,
    -0.06039987877011299,
    0.003340501105412841,
    -0.047403495758771896,
    -0.03688643127679825,
    -0.03700149059295654,
    -0.053056083619594574,
    -0.05178677290678024,
    -0.07945681363344193,
    0.13546644151210785,
    -0.020493559539318085,
    -0.004691308829933405,
    0.037861090153455734,
    -0.01941523514688015,
    0.018338583409786224,
    -0.023815065622329712,
    -0.10543563961982727,
    -0.04503227397799492,
    -0.07762259989976883,
    0.13431572914123535,
    -0.048526324331760406,
    -0.061330921947956085,
    0.0959298238158226,
    -0.031014179810881615,
    0.05313275381922722,
    -0.0719839558005333,
    -0.05459732934832573,
    -0.0352928526699543,
    0.04117381572723389,
    -0.0851721242070198,
    -0.005229719914495945,
    -0.08680060505867004,
    0.030464965850114822,
    0.007210718467831612,
    -0.022807270288467407,
    -0.003642191644757986,
    -0.044169239699840546,
    0.1343366503715515,
    -0.037768684327602386,
    -0.022599831223487854,
    0.021405477076768875,
    -0.050186172127723694,
    -0.04481957480311394,
    0.13360436260700226,
    -0.030876412987709045,
    0.10866472125053406,
    -0.04148414731025696,
    -0.036128003150224686,
    -0.05841571092605591,
    0.04962824285030365,
    0.12116242200136185,
    -0.04404192790389061,
    -0.02022683434188366,
    -0.048310089856386185,
    -0.06117050349712372,
    -0.07381816953420639,
    -0.058544766157865524,
    0.13888372480869293,
    -0.031188540160655975,
    0.008042979054152966,
    -0.017450258135795593,
    0.17051872611045837,
    0.0611182302236557,
    0.011935440823435783,
    -0.04694494605064392,
    0.0258637648075819,
    -0.05338040739297867,
    -0.04493812099099159,
    0.00251152366399765,
    -0.016679635271430016,
    -0.06427749991416931,
    -0.06282337754964828,
    -0.07139814645051956,
    -0.09689223021268845,
    -0.06830509006977081,
    -0.008947448804974556,
    -0.01614084467291832,
    -0.003454487770795822,
    0.049249958246946335,
    -0.014637933112680912,
    -0.03212312236428261,
    -0.04904940724372864,
    -0.045795924961566925,
    0.007000768557190895,
    0.09809184074401855,
    -0.06572813540697098,
    -0.07138072699308395,
    0.15009130537509918,
    -0.0591445118188858,
    -0.09300415217876434,
    -0.011687826365232468,
    0.1761537790298462,
    -0.031052516773343086,
    -0.037081699818372726,
    -0.010071570053696632,
    -0.05802517011761665,
    0.05669661611318588,
    0.007093995343893766,
    0.14679253101348877,
    0.03751586005091667,
    0.10104887932538986,
    0.05635138228535652,
    0.047030381858348846,
    -0.02637549117207527,
    -0.05404637008905411,
    0.01588364876806736,
    -0.024213530123233795,
    0.1647440493106842,
    0.0038148798048496246,
    -0.018709981814026833,
    0.042763907462358475,
    -0.09039932489395142,
    0.09996786713600159,
    -0.015323147177696228,
    -0.06260371953248978,
    0.1460253894329071,
    0.13356950879096985,
    0.014317121356725693,
    -0.02997150830924511,
    -0.0794777199625969,
    0.010234617628157139,
    0.05709412693977356,
    -0.044101227074861526,
    0.01887385919690132,
    0.034149061888456345,
    0.1482919454574585,
    -0.05490771681070328,
    0.014293570071458817,
    0.011256352998316288,
    -0.07384604960680008,
    -0.07562100887298584,
    0.007909595035016537,
    -0.057282425463199615,
    -0.054377663880586624,
    -0.030018610879778862,
    -0.008767150342464447,
    -0.007658748887479305,
    0.13149112462997437,
    -0.023679068312048912,
    0.02186751365661621,
    0.09421073645353317,
    -0.07069722563028336,
    0.03897340968251228,
    0.0047365641221404076,
    -0.016843540593981743,
    0.1021786704659462,
    0.22272728383541107,
    -0.04001608118414879,
    0.06574208289384842,
    0.028217565268278122,
    -0.08185591548681259,
    -0.043799612671136856,
    0.03905536234378815,
    -0.06420779973268509,
    0.03730490431189537,
    -0.061156563460826874,
    0.05924917012453079,
    -0.04711582139134407,
    -0.05842267721891403,
    -0.06460531055927277,
    -0.005869551096111536
  ]
}
All original generation metadata

Exact voice, prompts, model identifiers, source revision and usage records are preserved; credentials are excluded. These intended performance directions were withheld from the annotation models.

{
  "key": "de-00001",
  "language": "de",
  "language_tag": "de-DE",
  "voice": "Charon",
  "challenge_id": 1,
  "source": "existing_challenge_inspired",
  "title": "The Last Sunset on Our Beach",
  "instruction": "You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.",
  "inspired_by": "Relaxing on a Sandy Beach",
  "prompt_style_key": "burst",
  "prompt_style_label": "Sparse vocal response",
  "script": {
    "spoken_text": "Schau mal, wie golden das Wasser glänzt... fast genau wie damals. Der Sand unter uns ist noch ganz warm. Aber deine Hand... <breath> sie wird so kühl. Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht. Wie Zeit, die uns wegrutscht. Ich will nicht, dass die Sonne untergeht. <sob> Bitte... ich bin noch überhaupt nicht bereit dafür.",
    "beat_styles": [
      "tender and quiet",
      "trembling realization",
      "fragile despair"
    ],
    "intended_emotions": [
      "Sadness",
      "Grief",
      "Love"
    ],
    "scene_note": "A dying evening on the beach turns from comforting reminiscence to raw heartbreak as the speaker realizes their partner's life is slipping away.",
    "language_check": {
      "method": "langdetect",
      "top_language": "de",
      "top_probability": 1.0
    },
    "inline_tags": [
      "breath",
      "sob"
    ],
    "word_count": 57
  },
  "script_request": {
    "systemInstruction": {
      "parts": [
        {
          "text": "You are a skilled dialogue writer for naturalistic, close-microphone voice acting.\nWrite a short first-person monologue that could be spoken in a real situation. The actor is\nspeaking to another person or themselves, not reciting a plot synopsis. Preserve the\nchallenge's specific situation and emotional development while avoiding melodrama,\ngeneric motivational language, exposition dumps, and perfect studio-script polish.\nHuman emotion is mixed, context-dependent, and gradually changing. Include small,\ncredible imperfections or micro-distractions only when they fit. Spoken text should\nhave about 58-68 words, normally four to six sentences, so that a natural performance\ntakes roughly 26-32 seconds. Use punctuation, ellipses, and occasional exclamation\npoints only where the thought calls for them. Never write stage directions in brackets\nor in the spoken text: Gemini 3.8 TTS will read all ordinary words aloud.\nUse only documented inline tags for momentary human vocal events. No music, foley,\nor background sound tags. Do not overuse vocal tags. Write the spoken dialogue ONLY in natural German (de-DE), not English.\nPreserve the challenge situation rather than translating it literally.\nKeep the inline vocal-event tags in English for TTS. Use 60-75 spoken German words. Keep profanity and graphic content out of the script.\n\nRespond ONLY as a JSON object with keys:\n  spoken_text: a single verbatim transcript including optional inline vocal tags;\n  beat_styles: an array of one to three brief English delivery directions, in order;\n  intended_emotions: one to three of the 40 EmoNet emotion labels if known, otherwise\n    concise ordinary emotion words;\n  scene_note: one sentence on why the vocal choice fits the challenge.\n"
        }
      ]
    },
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "text": "CHALLENGE TITLE: The Last Sunset on Our Beach\nCHALLENGE INSTRUCTION: You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.\nPROMPT STYLE: Sparse vocal response\nSTYLE-SPECIFIC WRITING DIRECTION: Include one or two credible, situation-specific human non-speech vocal events as inline tags, such as <sigh>, <chuckle>, <gasp>, <groan>, <sob>, <grunt>, <breath>, or <short pause>. Only use an event that a real person in this scene might make; the scene still has coherent spoken language.\nDELIVERY BASELINE: naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis\nWrite all spoken words in natural German (de-DE). Make this a first-person monologue of roughly 30 seconds, with coherent speech and at most two restrained, situation-specific inline vocal-event tags. Choose human imperfections and flowing emotions only when they fit this exact challenge. Do not mention the studio voice or voice name in the dialogue."
          }
        ]
      }
    ],
    "generationConfig": {
      "responseMimeType": "application/json",
      "thinkingConfig": {
        "thinkingLevel": "low"
      },
      "temperature": 0.85
    }
  },
  "script_prompt": "CHALLENGE TITLE: The Last Sunset on Our Beach\nCHALLENGE INSTRUCTION: You're lying on the same beach where you fell in love thirty years ago, but this time your partner is dying beside you, their hand growing cold in yours. Begin with the sensory details—the warm sand, the waves—trying to hold onto peace, speaking softly about the beauty around you. But as you continue, let grief crack through your voice. The warmth of the sand becomes unbearable because they won't feel it tomorrow. Each wave that once brought peace now sounds like time running out. End with your voice breaking as you whisper that you're not ready, that no sunset has ever felt this heavy, transforming from attempted serenity into raw, vulnerable heartbreak.\nPROMPT STYLE: Sparse vocal response\nSTYLE-SPECIFIC WRITING DIRECTION: Include one or two credible, situation-specific human non-speech vocal events as inline tags, such as <sigh>, <chuckle>, <gasp>, <groan>, <sob>, <grunt>, <breath>, or <short pause>. Only use an event that a real person in this scene might make; the scene still has coherent spoken language.\nDELIVERY BASELINE: naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis\nWrite all spoken words in natural German (de-DE). Make this a first-person monologue of roughly 30 seconds, with coherent speech and at most two restrained, situation-specific inline vocal-event tags. Choose human imperfections and flowing emotions only when they fit this exact challenge. Do not mention the studio voice or voice name in the dialogue.",
  "script_system_prompt": "You are a skilled dialogue writer for naturalistic, close-microphone voice acting.\nWrite a short first-person monologue that could be spoken in a real situation. The actor is\nspeaking to another person or themselves, not reciting a plot synopsis. Preserve the\nchallenge's specific situation and emotional development while avoiding melodrama,\ngeneric motivational language, exposition dumps, and perfect studio-script polish.\nHuman emotion is mixed, context-dependent, and gradually changing. Include small,\ncredible imperfections or micro-distractions only when they fit. Spoken text should\nhave about 58-68 words, normally four to six sentences, so that a natural performance\ntakes roughly 26-32 seconds. Use punctuation, ellipses, and occasional exclamation\npoints only where the thought calls for them. Never write stage directions in brackets\nor in the spoken text: Gemini 3.8 TTS will read all ordinary words aloud.\nUse only documented inline tags for momentary human vocal events. No music, foley,\nor background sound tags. Do not overuse vocal tags. Write the spoken dialogue ONLY in natural German (de-DE), not English.\nPreserve the challenge situation rather than translating it literally.\nKeep the inline vocal-event tags in English for TTS. Use 60-75 spoken German words. Keep profanity and graphic content out of the script.\n\nRespond ONLY as a JSON object with keys:\n  spoken_text: a single verbatim transcript including optional inline vocal tags;\n  beat_styles: an array of one to three brief English delivery directions, in order;\n  intended_emotions: one to three of the 40 EmoNet emotion labels if known, otherwise\n    concise ordinary emotion words;\n  scene_note: one sentence on why the vocal choice fits the challenge.\n",
  "script_model": "gemini-3.8-flash",
  "script_usage": {
    "promptTokenCount": 720,
    "candidatesTokenCount": 197,
    "totalTokenCount": 917,
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 720
      }
    ],
    "serviceTier": "SERVICE_TIER_STANDARD"
  },
  "tts_model": "gemini-3.8-flash-tts",
  "tts_request": {
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "text": "Schau mal, wie golden das Wasser glänzt... fast genau wie damals. Der Sand unter uns ist noch ganz warm. Aber deine Hand... <breath> sie wird so kühl. Jede Welle klingt plötzlich nur noch wie ein Herzschlag, der verstreicht. Wie Zeit, die uns wegrutscht. Ich will nicht, dass die Sonne untergeht. <sob> Bitte... ich bin noch überhaupt nicht bereit dafür.",
            "speech_metadata": {
              "style": "naturalistic acting with one restrained, situation-appropriate vocal reaction; emotions flow without theatrical emphasis; tender and quiet"
            }
          }
        ]
      }
    ],
    "generationConfig": {
      "responseModalities": [
        "AUDIO"
      ],
      "speechConfig": {
        "voiceConfig": {
          "voice": "Charon"
        }
      }
    }
  },
  "tts_usage": {
    "promptTokenCount": 91,
    "candidatesTokenCount": 842,
    "totalTokenCount": 933,
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 91
      }
    ],
    "candidatesTokensDetails": [
      {
        "modality": "AUDIO",
        "tokenCount": 842
      }
    ],
    "serviceTier": "SERVICE_TIER_STANDARD"
  },
  "duration_s": 33.68,
  "audio_format": "mp3",
  "audio_bitrate_kbps": 128,
  "mp3_sha256": "3fdef6ec2a5e306994c4da3af5f0e7220b2804f49edf65b244d50c63493bb827",
  "voice_assignment_seed": 20261009,
  "voice_assignment": "balanced per language and source subset",
  "source_repo": "https://github.com/LAION-AI/Voice-Acting-Pipeline",
  "source_commit": "e3b0d5281558f7f73e1118c6a9b4c149bb8c0621",
  "source_file_sha256": "7ef759b639ef3646ca8a985664747e37ad93e4ab989f97dd8cece4f5201ceb6f"
}
Annotation provenance and frozen settings
{
  "humaneness_model": "laion/humaneness-ears-base-medium",
  "humaneness_revision": "818506970d93c809a3295a0a02c40dee8ff2bfc3",
  "caption_models": {
    "voice_repo": "laion/moss-voice-captioner",
    "voice_revision": "07cad84c33c676ea37979fb7ee844fd06ad92a76",
    "burst_repo": "laion/moss-vocal-burst-captioner",
    "burst_revision": "434e38f7e3eeaac0de4d3325b93a924e441153ce"
  },
  "original_inference_code_commit": "a6a286f",
  "procedural_format_code_commit": "dd1ac6e3b8bc2574b164680040b81703a1f3696f",
  "normalization_sha256": "c96503622619f63528a12ddcb28b19d4beddff7d94522d11b6a83dabad654f43",
  "original_pipeline_settings_hash": "1cbe629c2f1aef34160f2482e398fba26d131e90c582fff2f767158560a90916",
  "release_format_hash": "731fb1dee60a16c4a665fa556cacb5266d0aa303366b3741cd0a7b4b8298b8fc",
  "prompt_format_id": "humaneness-general-script-v2-20261011",
  "machine_generated_not_human_verified": true,
  "canonical_mixed_reference_z_and_caption_speech_reference_z_are_separate": true,
  "raw_source_archives_unchanged": true
}

Download the full annotation JSON · Voice caption weights/code · Burst caption weights/code · Humaneness Ears Medium